code: def MSE(
      y_true: "ndarray",
      y_pred: "ndarray",
      multioutput: str = "uniform_average",
  ) -> Union[float64, "ndarray"]:
      """
      Calculate the mean squared error (MSE).
  
      .. math::
  
          \\text{MSE} = \\frac{1}{n}\\sum_{t=1}^n (y_t-\\hat{y_t})^2
  
      :param y_true: Array-like of shape = (n_samples, \*).
             Ground truth (correct) target values.
      :param y_pred: Array-like of shape = (n_samples, \*).
             Estimated target values.
      :param multioutput: String in ['raw_values', 'uniform_average']
  
      :return: Float or ndarray of floats.
               A non-negative floating point value (the best value is 0.0), or an
               array of floating point values, one for each individual target.
      """
      y_true, y_pred, original_shape = _standardize_input(
          y_true, y_pred, multioutput
      )
      result = mean_squared_error(
          y_true, y_pred, multioutput=multioutput
      )
      if multioutput == "raw_values":
          return result.reshape(original_shape)
      return result
  
imports: from sklearn.metrics import mean_squared_error
  from sklearn.metrics import r2_score
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_log_error
  import numpy
  import pandas
  from bigdl.dllib.utils.log4Error import *
  from bigdl.dllib.utils.log4Error import invalidInputError
  from numpy import float64, ndarray
  from typing import List, Tuple, Union, Optional, Callable, Any
label_desc: Use image node_id 13 for calling a global function with example usage: MSE(y_true, y_pred, multioutput) and returns: result, result